Classic GM algorithms employed in computer vision, are considered for the fine registration. We account for Graduated Assignment (GA) [1]}, Spectral Matching (SM) [2]}, Spectral Matching with Affine Constraints (SMAC) [3]}, Probabilistic Matching (PM) [4]}, Integer Projected Fixed Point (IPFP-U/SM) [5]}, Re-weighted Random Walk Matching (RRWM) [6]}, and the current state-of-the-art, the non-rigid Factorized Graph Matching (FGM) [7]}. The deformable graph matching problem, detailed in [7]}, formulates the unknown graph correspondence being constrained with a geometric transformation \(T\) . A composition of transformations (i.e. similar, affine, and non-rigid) are incorporated into the compatibility function (eq1), and subsequently estimated by optimising jointly the correspondence matrix \(\mathbf {X}\)  and the composite transformation \(T\)  itself. We employed the undirected-graph versions of the listed algorithms. Implementations and configurations are available from authors' websites.
